Black-Box Models - Edgentiq

Safeguarding LLMs: A New Framework for In-Context Unlearning

Enhancing AI Model Understanding with Smarter Data Sampling

Auditing AI Fairness with Incomplete Data: A New Approach to Cost Efficiency

The Causal Key to Verifying Machine Unlearning

Bridging the Explanatory Divide: How V-CECE Reveals AI’s Understanding of Visual Concepts

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Enhancing Trust in Multimodal AI Predictions with Functional Sampling

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Evo-Merging: Combining Black-Box Language Models for Enhanced AI Services

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Unveiling Causal Importance: A New Approach to Evaluating Explainable AI for Boolean Logic

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Predicting AI Reliability: Token-Entropy Conformal Prediction for Language Models

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Beyond the Text: Probing Implicit Knowledge in Black-box Language Models

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Making Sense of AI: A Hybrid Approach to Explainable Machine Learning

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Optimizing LLM Prompts with Awareness of Inference Strategies

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Unveiling a New Approach to Adversarial Attacks on Multi-Task AI Models

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HIA: A Budget-Friendly Approach to Aligning Large Language Models

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Detecting LLM Hallucinations by Anticipating Future Text

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Uncovering Specific Causes: New Algorithms for Explaining AI Decisions

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Safeguarding LLMs: A New Framework for In-Context Unlearning

November 11, 2025

Enhancing AI Model Understanding with Smarter Data Sampling

October 31, 2025

Auditing AI Fairness with Incomplete Data: A New Approach to Cost Efficiency

October 7, 2025